Analisis Sentimen pada Ulasan Aplikasi Maxim di Google Play Store dengan K-Nearest Neighbor
 (*)Restu Ramadhan  (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
M Afdal (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
Inggih Permana (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
Muhammad Jazman (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
(*) Corresponding Author |
Abstract
Online transportation is an innovation in emerging technology to solve various problems that arise in conventional public transportation such as in the ease of ordering, availability, and digitization of payments. Maxim is an online transportation company that has been operating since 2018 in Indonesia. As the number of users of the maxim application increases, demands for the quality of application service also increase. In the Google Play Store, reviews and information about an app are stored in text form. One of the processes of extracting text mining information in the text category is Sentiment Analysis to see the tendency of a sentiment or opinion whether it is positive, neutral, or negative at the Maxim application user reviews. The sentiment classification process using the K-NN algorithm produces accuracy, precision, and recall of 90.23%; 90.23%; and a recall value of 72.38% with an experiment using 90% training data, 10% test data, and a value of k = 5.
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References
A. Popy, “RESOLUSI KONFLIK ANTARA PENGEMUDI TRANSPORTASI ONLINE DENGAN PENGEMUDI TRANSPORTASI KONVENSIONAL (Studi Hambatan Struktural Penyelesaian Konflik antara Pengemudi Gojek, Pengemudi Angkutan Kota (Angkot), Pengemudi Ojek Pangkalan di Kota Padang),” Universitas Andalas, 2018.
A. POPY, “e-Conomy SEA 2018 Southeast Asia’s internet economy hits an inflection point.” pp. 1–32, 2018.
J. P. Kompetitif, “Laporan Survei Penetrasi & Profil Perilaku Pengguna Internet Indonesia 2018,” J. Pemasar. Kompetitif, vol. 1, no. 3, pp. 71–80.
Desy Setyowati, “Asing hingga Lokal, Ini Lima Pesaing Gojek dan Grab di Indonesia - Teknologi Katadata.co.id,” katadata, Aug. 07, 2019. https://katadata.co.id/desysetyowati/digital/5e9a503aab501/asing-hingga-lokal-ini-lima-pesaing-gojek-dan-grab-di-indonesia (accessed Jul. 03, 2023).
F. Al-Rasyid, “‘Resahkan’ Kompetitor, Maxim Ternyata Bukan Perusahaan Ojek Online - Russia Beyond,” 2019. https://id.rbth.com/economics/81796-maxim-ojol-asal-rusia-wyx (accessed Jul. 03, 2023).
M. S. Alrajak, I. Ernawati, and I. Nurlaili, “Analisis Sentimen Terhadap Pelayanan PT PLN di Jakarta pada Twitter dengan Algoritma K- Nearest Neighbor (K-NN),” Semin. Nas. Mhs. Ilmu Komput. dan Apl., vol. 1, no. 2, pp. 110–122, 2020.
B. Liu, Sentiment analysis and opinion mining. Springer Nature, 2022.
O. Somantri and D. Apriliani, “Support Vector Machine Berbasis Feature Selection Untuk Sentiment Analysis Kepuasan Pelanggan Terhadap Pelayanan Warung dan Restoran Kuliner Kota Tegal,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 5, p. 537, 2018, doi: 10.25126/jtiik.201855867.
A. M. Pravina, I. Cholissodin, and P. P. Adikara, “Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine (SVM),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 3, pp. 2789–2797, 2019, [Online]. Available: http://j-ptiik.ub.ac.id.
N. Tri Romadloni, I. Santoso, and S. Budilaksono, “Perbandingan Metode Naive Bayes, Knn Dan Decision Tree Terhadap Analisis Sentimen Transportasi Krl Commuter Line,” J. IKRA-ITH Inform., vol. 3, no. 2, pp. 1–9, 2019.
N. S. N. Salam, A. A. Supianto, and A. R. Perdanakusuma, “Analisis Sentimen Opini Mahasiswa Terhadap Saran Kuesioner Penilaian Kinerja Dosen dengan Menggunakan TF-IDF dan K-Nearest Neighbor,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 6, pp. 6148–6156, 2019.
A. P. Giovani, A. Ardiansyah, T. Haryanti, L. Kurniawati, and W. Gata, “Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi,” J. Teknoinfo, vol. 14, no. 2, pp. 115–123, 2020.
S. Ernawati and R. Wati, “Penerapan Algoritma K-Nearest Neighbors Pada Analisis Sentimen Review Agen Travel,” J. Khatulistiwa Inform., vol. 6, no. 1, pp. 64–69, 2018.
A. N. S. Rahayu, T. I. Hermanto, and I. M. Nugroho, “Sentiment Analysis Using K-Nearest Neighbor Based on Particle Swarm Optimization According To Sunscreen’S Reviews,” J. Tek. Inform., vol. 3, no. 6, pp. 1639–1646, 2022, doi: 10.20884/1.jutif.2022.3.6.425.
M. R. Firdaus, F. M. Rizki, F. M. Gaus, and I. K. Susanto, “Analisis Sentimen Dan Topic Modelling Dalam Aplikasi Ruangguru,” J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 4, no. 1, p. 66, 2020, doi: 10.30645/j-sakti.v4i1.188.
A. Deviyanto and M. D. R. Wahyudi, “Penerapan analisis sentimen pada pengguna twitter menggunakan metode K-Nearest Neighbor,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 3, no. 1, pp. 1–13, 2018.
U. D. Arni, “Garuda Cyber | Artikel,” garuda cyber, 2018. https://garudacyber.co.id/artikel/1254-apa-itu-text- mining (accessed Jul. 03, 2023).
J. Enterprise, Python untuk Programmer Pemula. Elex media komputindo, 2019.
R. Sari, “Analisis Sentimen Pada Review Objek Wisata Dunia Fantasi Menggunakan Algoritma K-Nearest Neighbor (K-Nn),” EVOLUSI J. Sains dan Manaj., vol. 8, no. 1, pp. 10–17, 2020, doi: 10.31294/evolusi.v8i1.7371.
A. M. Ismail, “Cara Kerja Algoritma k-Nearest Neighbor (k-NN),” Medium, 2018. https://medium.com/bee-solution-partners/cara-kerja-algoritma-k-nearest-neighbor-k-nn-389297de543e (accessed Jul. 03, 2023).
D. Era, S. Andryana, and A. Rubhasy, “Perbandingan Algoritma Na{"i}ve Bayes Dan K-Nearest Neighbor pada Analisis Sentimen Pembukaan Pariwisata Di Masa Pandemi Covid 19,” J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 7, no. 1, pp. 263–272, 2023.
P. H. Prastyo, A. S. Sumi, A. W. Dian, and A. E. Permanasari, “Tweets responding to the Indonesian Government’s handling of COVID-19: Sentiment analysis using SVM with normalized poly kernel,” J. Inf. Syst. Eng. Bus. Intell, vol. 6, no. 2, p. 112, 2020.
R. Prakoso, “kbba,” 2017. https://github.com/ramaprakoso/analisis-sentimen/blob/master/kamus/kbba.txt. (accessed Juny. 11, 2023)
M. ARIFAH, “Analisis Sentimen Terhadap Produk Tanaman Transgenik Menggunakan Lexicon-Based Dan Naive Bayes,” 2019.
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